Track 14. Artificial Intelligence and Smart Learning Environments (AISLE@ICALT2017)
Track Program Chairs
Track Description and Topics of Interest
Broadly defined, the Artificial Intelligence and Smart Learning Environments represent a new wave of educational systems, involving an effective and efficient interplay of pedagogy, technology and their fusion towards the betterment of learning processes. Various components of this interplay include but are not limited to:
- Pedagogy/didactics: instructional design, learning paradigms, teaching paradigms, environmental factors, assessment paradigms, social factors, policy
- Technology: emerging technologies, innovative uses of mature technologies, interactions, adoption, usability, standards, and emerging/new technological paradigms (open educational resources, learning analytics, cloud computing, smart classrooms, etc.)
- Fusion of pedagogy/didactics and technology: transformation of curriculum, transformation of teaching behaviour, transformation of learning, transformation of administration, transformation of schooling, best practices of infusion, piloting of new ideas
A learning environment can be considered smart when the learner is supported through the use of adaptive and innovative technologies from childhood all the way through formal education, and continued during work and adult life where non-formal and informal learning approaches become primary means for learning. Smart learning environments are neither pure technology-based systems nor a particular pedagogical approach. They encompass various contexts, in which students (and perhaps teachers) move from one context to another. So, they are perhaps overarching concept for future academia. This perspective has the potential to overcome some of the traditions of institution based instruction towards lifelong learning.
AISLE@ICALT2017 will explore various dimensions of the application of artificial intelligence and the emerging smart learning environments, such as what makes a learning environment smart, challenges in the design and implementation of such environments in multiple and heterogeneous contexts, pedagogical and technological underpinnings, and the validation issues.
Track Program Committee
Ben Chang, National Central University, Taiwan
Bernardo Tabuenca, Universidad Politécnica de Madrid, Spain
Bertrand David, LIRIS-CNRS, France
Dirk Börner, Open University of Netherlands, The Netherlands
Gilbert Paquette, Université du Québec à Montréal, Canada
Jia-Jiunn Lo, Chung Hua University, Taiwan
Jinghua Zhang, Winston-Salem State University, USA
Johannes Konert, Technische Universitat Darmstadt, Germany
Josep Blat, Universitat Pompeu Fabra, Spain
Ju-ling Shih, National University Of Tainan, Taiwan
Tosti Hsu-Cheng Chiang, National Taiwan Normal University, Taiwan
Chin-Yeh Wang, National Central University, Taiwan
Ben Liu, National Cheng Kung University, Taiwan
Gwo-Haur Hwang, Ling Tung University, Taiwan
Pei-Chen Sun, National Kaohsiung Normal University, Taiwan
George Ghinea, Brunel University, UK
Jun-Ming Su, National University of Tainan, Taiwan
Li Ping, Hong Kong Institute of Education, Hong Kong
Matej Zajc, University of Ljubljana, Slovenia
Ting-Wen Chang, Beijing Normal University, China
Yanjie Song, The Hong Kong Institute of Education, Hong Kong
Important Dates about ICALT 2017 can be found here.
The ICALT 2017 Author Guidelines can be found here.
The ICALT 2017 CfP can be found here.